Color is the Most Demonstrative Visual Feature and Studied in the Context of CBIR

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Color is widely remarked as one of the most demonstrative visual features, and as such it has been largely studied in the context of CBIR, thus number one to a rich variety of descriptors. As traditional color features used in CBIR, there are color histogram, color correlogram, and dominant color descriptor (DCD) [1,3,4]. A simple color similarity between two images can be measured by comparing their color histograms. The color histogram, which is a common color descriptor, indicates the occurrence frequencies of colors in the image. The color correlogram describes the probability of finding color pairs at a fixed pixel distance and provides spatial information. Therefore color correlogram yields better retrieval accuracy in comparisonto color histogram [3]. DCD is MPEG-7 color descriptors. DCD describes the salient color distributions in an image or a region of interest, and provides an effective, compact, and intuitive representation of colors presented in an image. However, DCD similarity matching does not fit human perception very well, and it will cause incorrect ranks for images with similar color distribution [5]. In Ref. [6], Yang et al. presented a color quantization method for dominant color extraction, called the linear block algorithm (LBA), and it has been shown that LBA is efficient in color quantization and computation. For the purpose of effectively retrieving more similar images from the digital image databases (DBs), Lu et al. [7] uses the color distributions, the mean value and the standard deviation, to represent the global characteristics of the image, and the image bitmap is used to represent the local characteristics of the image for increasing the accuracy of the retrieval system. Aptoula et al. [8] presen...

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...echniques are introduced. Hiremath et al. [22,23] presented novel retrieval frameworks for combining multiple image information, in which the local color and texture descriptors are captured in a coarse segmentation framework of grids.
In this paper, we propose a new content-based image retrieval technique using Zernike chromaticity distribution momentsand rotation-scale invariant Contourlet texture feature, which achieves higher retrieval efficiency. The rest of this paper is organized as follows. Section 2 presents Zernike chromaticity distribution color moments extraction. Section 3 describes the Contourlet transform and rotation-scale invariant texture representation. Section 4 contains the description of similarity measure for image retrieval. Simulation results in Section 5 will show the performance of our scheme. Finally,Section 6 concludes this presentation.

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